Learn ggplot2 with DataCamp

Learn more about ggplot2 in R: https://www.datacamp.com/courses/data-visualization-with-ggplot2-part-3 Welcome to the third course in Data Camp's series on Data visualisation with ggplot2. This course assumes you're familiar with the functions and concepts in data visualisation that I introduced in my first two courses. Those courses should have given you a pretty good idea about what good data visualisation is, and how to achieve it. Since this is an advanced course, we're going to dig a bit deeper into some more advanced ggplot2 functions, but before we get to that we'll cap off the material we began in the first two courses by rounding out your knowledge with some niche topics. We'll begin by exploring two kinds of specialised plots. In chapter 1, we'll consider the first type of specialised plots - those suited for a data-savvy audience. These are statistical plots that you wouldn't normally see the popular press, like box plots and density plots. plus, we'll consider how to combine several variables. In chapter 2 we'll move onto the second type of specialised plots, those that are suited for very specific data types. We'll begin by putting some fundamental concepts of working with large datasets into perspective. And then we'll see some specific cases, like ternary plots, networks, and diagnostic plots This topic will continue into chapter 3 when we consider the two main classes of maps: Choropleths, and cartographic maps, Finally we'll see many concepts come together with the last type of specialised plot, animations, which adds video frames as another mapping aesthetic. Depending on your area of expertise, you may find that you seldom have the need to use these specialised plot types - but it's still useful to know what's possible within the ggplot2 framework. In the fourth chapter we're going to get under the hood by digging into the internals of ggplot2 objects. For this, we'll begin by looking at the basics of the grid package, on which ggplot2 is built. The next step is to begin manipulating graphical objects that we've made with ggplot2 and look at a more efficient way of doing that with some built-in functions in ggplot2, such as ggplot_build. In the last part we'll make use of a useful accessory package called gridExtra. In the fifth chapter, we'll bring our series on ggplot2 to a close with two case studies. In the first case study, we'll look at a feature that was introduced in ggplot2 release two point Oh - making extensions. We'll understand how to build a new geom or stats function from scratch. This will allow us to use ggplot2 to create exactly the statistics and visualisations we want in a more straight-forward manner.